write-method

A skill for writing the method section of an academic paper in LaTeX. The method section explains the proposed approach, comparison methods, and experimental setup.

In plain words
What is it for?
Use it after the derivation has converged and contributions are recorded to describe the approach, baselines, and setup for a specified venue.
Why use it?
It converts a completed research derivation and contribution structure into an organized technical description for a paper.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/datalab-atom/evoany/write-method
Any agent
npx skills add DataLab-atom/EvoAny --skill write-method
Clone the repo
git clone --depth 1 https://github.com/DataLab-atom/EvoAny

Made for: Claude Code, Codex.

Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 802 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00029 $0.00802
Opus 5 $0.00015 $0.00401
Sonnet 5 $0.00006 $0.00160
Haiku 4.5 $0.00003 $0.00080

Measured 2d ago against content hash eb0941fe7c03, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

write-method scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugin/skills/write-method/SKILL.md · 105 lines

How it starts

The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/write-method — Method Chapter Writing

D1: Method chapter generation — transforms the derivation forest's deep motivation and contribution structure into a rigorous methodology LaTeX section.

Purpose

Read the completed derivation forest (from C-layer), extract the deep motivation Q and primary contribution branches, and generate a well-structured Method section in LaTeX format suitable for academic submission.

Usage

/write-method <forest_id> [--venue <venue_name>]

Examples:

  • /write-method exp-2024-run-01 --venue NeurIPS
  • /write-method my-forest --venue ICML

Prerequisites

Before running this skill, ensure:

  1. The derivation forest has converged (status = "converging" or "done")
  2. At least one convergence point has been verified
  3. All contributing branches have been recorded via research_record_contribution

Behavior

Step 1: Read the Derivation Forest

  1. Call research_get_forest(forest_id) to retrieve the full forest state
  2. Extract:
    • All convergence_points with verification_status === "verified"
    • For each point: the deep question Q, contributing nodes, literature references
    • All contributions with level === "primary"

Step 2: Synthesize the Method Chapter

Based on the forest data, generate a LaTeX method chapter covering:

  1. Problem Formalization — What is the deep problem Q being solved?
  2. Technical Approach — How does the evolved code address Q?
  3. Key Mechanisms — What are the primary contributions? (from converged branches)
  4. Relationship to Existing Methods — How does this differ from related work?

Step 3: Write to File

  1. Determine output path: <repo>/research/paper/sections/method.tex
  2. Call research_get_forest with the forest ID to get the repo path
  3. Write the LaTeX content to the file
  4. Call bib_append to add any new citations from the forest's literature references

Output Format

\section{Method}
\label{sec:method}

\subsection{Problem Definition}
% Content addressing the deep motivation Q

\subsection{Technical Approach}
% How the evolved approach works

\subsection{Key Mechanisms}
% Primary contributions from converged branches

\subsection{Theoretical Analysis (if applicable)}
% Any formal guarantees or analysis

Read the full file on GitHub · 105 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 105 lines · 29 tokens per session scan A eb0941fe7c03

Subscribe to this mod's changes

write-method is a skill published in the GitHub repository DataLab-atom/EvoAny (37 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 29 tokens to every session and 802 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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